The present invention relates generally to a field of imaging systems. In particular, the invention relates to a technique for analyzing image data to recognize features of interest and comparing resulting analyses with similar analyses performed on image data collected at different points in time in computationally and workflow-efficient manners.
Many applications exist for digital imagery. Such applications range from medical diagnostic imaging to part detection and analysis, parcel screening, and so forth. Similarly, many different types of imaging systems are currently available, some of which span certain of these applications. Imaging systems range from traditional photographic systems to much more complex magnetic resonance imagine (MRI) system, computed tomography (CT) systems, positron emission tomography (PET) systems, ultrasound systems, X-ray systems, and so forth. In each of these systems, some type of image data acquisition circuitry detects input data which is used to codify individual picture elements or pixels in a matrix. When reconstructed, the pixels can be viewed in a composite image which is useful to the viewer for various intended purposes.
Regardless of the origin of pixilated image data, many new uses are being explored which enhance the usefulness of the data for various purposes. For example, in medical imaging, as well as in other fields, such as parcel inspection, image data is analyzed to recognize structures encoded in the pixels, that may be representative of features of particular interest. In the medical field these may include specific anatomies, anomalies, pathologies, and so forth. In automated computer aided or computer assisted processes, computers can now identify certain such features which can be highlighted to a user to augment or aid in diagnosis and treatment of disease, or to analyze various states of wellness. Similarly, in other contexts, such automated recognition and classification processes can greatly assist human viewers and readers by pointing out potential objects of concern or interest.
In many contexts, particularly in medical imaging, images are created of the same subject or anatomy at different points in time. Certain of these images may depict anatomies or anomalies, such as growths, lesions, or other conditions which change over time. The detection of change in medical images of a patient acquire two different instances in time would be of great potential for improving diagnosis and treatment of disease, and for monitoring response to such treatment. More generally, however, such change can be useful in tracking development and growth, or for providing an indication of any meaningful change overtime, both within and outside the medical context. Certain, “temporal subtraction” applications have been proposed. In certain such applications dissimilarity between images is calculated using a simple pixel-by-pixel subtraction approach of registered images. However, simple subtraction results in images of poor contrast. Moreover, such approaches are not sufficiently robust when two initial images are acquired using different techniques or modalities. Moreover, such approaches do not incorporate an indication of a confidence level in the magnitude of the dissimilarity measurement.
In a temporal change image, resulting pixel values, which may be displayed as gray levels in a monochrome image, or proportional to the difference or dissimilarity in pixel values between two input images acquired with temporal separation. The input images may require registration and may be processed to compensate for several factors, such as the difference in positioning of the subject during two image acquisition sessions, differences in acquisition parameters, differences in bit resolution of the images, and differences in any pre- or post-processing that may have been applied to images. Any errors in registration of the two images may result in significantly large values in the dissimilarity image due to the presumption that much more significant changes have occurred in the images or between the images due to the misalignment. For example, if the resulting registration is not perfect, the temporal analysis image of the subject resulting from two identical images will not be a zero-value image as would be anticipated given the identity of the images. That is, for identical images, the process should result in no contrast whatsoever in the dissimilarity image. These non-zero elements of the dissimilarity image represent artifacts that could be mistaken for temporal change in the subject. Such artifacts and the lack of standard anatomical features renders radiographic interpretation of temporal subtracted images challenging for a radiologist or other user, especially when given the unfamiliarity of such users with the appearance of such images. In general, a dissimilarity image summarizes only differences between two compared images. Thus, unlike conventional images that reproduce aspects of a subject in an intuitive manner, this similarity images will generally only illustrate changes in the subject as dark or light regions, lines, and so forth. The images can, of course, be superimposed or otherwise associated with the original images, although developments in the field have not risen to a level as yet to satisfactory in this regard.
The advent and proliferation of digital imaging has enabled rapid electronic access to a variety of information, particularly patient information in the medical field, and the ability to perform rapid advanced image processing and analysis. For example, integration of digital acquisition coupled to a data repository in a hospital or other network enables rapid calculation and display of temporal change images. In addition, these technologies have enabled the practical use of computer aided detection and diagnosis (CAD) techniques and radiology. Such techniques generally serve to identify and classify various features of interest reproduced or detectable within the image data. Such “machine vision” tools have been developed to improve sensitivity, specificity and efficiency of radiologists in the interpretation of images.
Little or nothing has been done in the field, however, as yet for enhancing the utility of temporal change images, that is, images compared to one another and analyzed to detect evolution of features or other changes within the images. There is a need, at present, for further enhancement in the existing techniques, and creation of new techniques for performing complex analyses of images taken at different points in time so as to provide a useful indication of changes occurring in an image subject.